Particle arc therapy

ABSTRACT

A method of optimizing delivery of a particle beam at a target is disclosed. The particle beam is delivered from an output device at a plurality of control points. In implementations, the method comprises delivering a substantially continuous particle beam about the plurality of control points, iteratively adjusting a delivery time of the substantially continuous particle beam about the plurality of control points, and processing to undertake at least one of (i) pre-defining energy layers based on one or both of the control points and a control point sampling frequency, or (ii) sorting the energy layers.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation-in-part of U.S. applicationSer. No. 16/083,354, filed Sep. 7, 2018, which claims the benefit of PCTInternational Application No. PCT/US2017/021837, filed Mar. 10, 2017,which claims the benefit under 35 U.S.C. § 119(e) to U.S. ProvisionalApplication 62/410,674, filed on Oct. 20, 2016, U.S. ProvisionalApplication 62/337,097, filed on May 16, 2016, U.S. ProvisionalApplication 62/306,403, filed on Mar. 10, 2016, and U.S. ProvisionalApplication 62/306,413, filed on Mar. 10, 2016. The disclosures of theseprior applications are considered part of this disclosure and are herebyincorporated by reference in their entireties.

TECHNICAL FIELD

This disclosure relates to systems and methods for providingsubstantially continuous charged particle arc therapy.

BACKGROUND

Charged particle therapy employs beams of energized protons, carbonions, or other charged particles. Currently, one of the most commontypes of particle therapy is proton therapy. Proton therapy, alsoreferred to as proton beam therapy, is a medical procedure that uses abeam of protons to irradiate diseased tissue. One of the advantages ofproton therapy in comparison to the conventional photon radiotherapysuch as, X-ray or gamma ray, for example, for the treatment of cancer,is the reduced integral dose to the patient. Integral dose can refer toa total amount of energy experienced by the patient during radiativekinds of treatments. Proton therapy can help to minimize damage totissues and structures while focusing a preferred dose upon the targettissue.

Proton therapy may provide superior tumor coverage and deliver a lowerintegral dose to a patient's body compared to conventional radiotherapy.As compared to traditional passive-scattering proton therapy,spot-scanning proton therapy techniques may provide superior targetcoverage by scanning the target spot-by-spot and layer-by-layer similarto three-dimensional printing techniques. But current spot-scanningproton therapy beam delivery techniques may be limited in performanceand may only be capable of delivering limited proton beams in onetreatment fraction (i.e., normally one treatment fraction only consistsof 1-4 treatment fields).

SUMMARY

One aspect of the disclosure provides a method of optimizing delivery ofa particle beam at a target, wherein the particle beam is delivered froman output device at a plurality of control points. The method comprisesdelivering a substantially continuous particle beam about the pluralityof control points, iteratively adjusting a delivery time of thesubstantially continuous particle beam about the plurality of controlpoints, identifying an initial set of control points, and providing afirst group of optimized beams, wherein each optimized beam of the firstgroup of optimized beams is configured to be output at a first controlpoint and steered towards one or more energy layers that are associatedwith one or more monitor unit (MU), providing a second group ofoptimized beams, each optimized beam of the second group of optimizedbeams configured to be output at the first control point and having areduced number of energy layers, increasing a number of the initialcontrol points, and maintaining a total number of energy layers as oneof equal to, more than, or less than a total number of energy layersassociated with the initial control points during movement of the beambetween the plurality of control points, wherein increasing the numberof initial control points comprises adding one or more adjacent controlpoints to each initial control point, the adjacent control point havinga fraction number of the total energy layers or MU of the initialcontrol point.

Implementations of the disclosure may include one or more of thefollowing optional features. In some implementations, the delivery timeof the substantially continuous particle beam is iteratively adjustedvia one or more of filtering the one or more energy layers, merging theone or more energy layers, re-distributing the one or more energylayers, or adding new energy layers to the one or more energy layers.

The delivery time of the substantially continuous particle beam may beiteratively adjusted via one or more of filtering one or more protonspots, merging the one or more proton spots, re-distributing the one ormore proton spots, or adding new proton spots within and between the oneor more proton spots.

The delivery time of the substantially continuous particle beam may beiteratively adjusted via adjusting one or more of the MU of one or moreproton spots within and between the control points or an intensity ofthe one or more proton spots within and between the control points.

The delivery time of the substantially continuous particle beam may beat least partially determined based on a machine-specific deliverysequence model (DSM_(arc)) that is calculated using a plurality ofmachine specific system parameters and a plurality of user definedparameters. The plurality of machine specific system parameters mayinclude a delivery tolerance window, gantry mechanical limitations, andmechanism of control point connections. The plurality of user definedparameters may include a defined total SPArc delivery time or speed, adelivery tolerance window, and momentum or speed change during deliveryof the substantially continuous particle beam.

Another aspect of the disclosure provides a method of optimizingdelivery of a particle beam at a target, wherein the particle beam isdelivered from an output device at a plurality of control points. Themethod comprises delivering a substantially continuous particle beamabout the plurality of control points, iteratively adjusting a deliverytime of the substantially continuous particle beam about the pluralityof control points, and processing to undertake at least one of (i)pre-defining energy layers based on one or both of the control pointsand a control point sampling frequency, or (ii) sorting the energylayers.

This aspect may include one or more of the following optional features.In some implementations, the delivery time of the substantiallycontinuous particle beam is iteratively adjusted via one or more offiltering the one or more energy layers, merging the one or more energylayers, re-distributing the one or more energy layers, or adding newenergy layers to the one or more energy layers.

The delivery time of the substantially continuous particle beam may beiteratively adjusted via one or more of filtering one or more protonspots, merging the one or more proton spots, re-distributing the one ormore proton spots, or adding new proton spots within and between the oneor more proton spots.

The delivery time of the substantially continuous particle beam may beiteratively adjusted via adjusting one or more of the MU of one or moreproton spots within and between the control points or an intensity ofthe one or more proton spots within and between the control points.

The delivery time of the substantially continuous particle beam may beat least partially determined based on a machine-specific deliverysequence model (DSM_(arc)) that is calculated using a plurality ofmachine specific system parameters and a plurality of user definedparameters. The plurality of machine specific system parameters mayinclude a delivery tolerance window, gantry mechanical limitations, andmechanism of control point connections. The plurality of user definedparameters may include a defined total SPArc delivery time or speed, adelivery tolerance window, and momentum or speed change during deliveryof the substantially continuous particle beam.

Another aspect of the disclosure provides a system comprising dataprocessing hardware, and memory hardware in communication with the dataprocessing hardware. The memory hardware stores instructions that whenexecuted on the data processing hardware cause the data processinghardware to perform operations comprising delivering a substantiallycontinuous particle beam at a target about a plurality of controlpoints, iteratively adjusting a delivery time of the substantiallycontinuous particle beam about the plurality of control points,identifying an initial set of control points, providing a first group ofoptimized beams, wherein each optimized beam of the first group ofoptimized beams is configured to be output at a first control point andsteered towards one or more energy layers that are associated with oneor more monitor unit (MU), providing a second group of optimized beams,each optimized beam of the second group of optimized beams configured tobe output at the first control point and having a reduced number ofenergy layers, increasing a number of the initial control points, andmaintaining a total number of energy layers as one of equal to, morethan, or less than a total number of energy layers associated with theinitial control points during movement of the beam between the pluralityof control points, wherein increasing the number of initial controlpoints comprises adding one or more adjacent control points to eachinitial control point, the adjacent control point having a fractionnumber of the total energy layers or MU of the initial control point.

This aspect may include one or more of the following optional features.In some implementations, the delivery time of the substantiallycontinuous particle beam is iteratively adjusted via one or more offiltering the one or more energy layers, merging the one or more energylayers, re-distributing the one or more energy layers, or adding newenergy layers to the one or more energy layers.

The delivery time of the substantially continuous particle beam may beiteratively adjusted via one or more of filtering one or more protonspots, merging the one or more proton spots, re-distributing the one ormore proton spots, or adding new proton spots within and between the oneor more proton spots.

The delivery time of the substantially continuous particle beam may beiteratively adjusted via adjusting one or more of the MU of one or moreproton spots within and between the control points or an intensity ofthe one or more proton spots within and between the control points.

The delivery time of the substantially continuous particle beam may beat least partially determined based on a machine-specific deliverysequence model (DSM_(arc)) that is calculated using a plurality ofmachine specific system parameters and a plurality of user definedparameters. The plurality of machine specific system parameters mayinclude a delivery tolerance window, gantry mechanical limitations, andmechanism of control point connections.

The details of one or more implementations of the disclosure are setforth in the accompanying drawings and the description below. Otheraspects, features, and advantages will be apparent from the descriptionand drawings, and from the claims.

DESCRIPTION OF DRAWINGS

FIG. 1 is a schematic view of an exemplary system according toimplementations.

FIG. 2A is an example arrangement of operations for operating the systemof FIG. 1 .

FIG. 2B is an example arrangement of operations for operating the systemof FIG. 1 .

FIGS. 3A-3D illustrate exemplary arrangements of operations foroperating the system of FIG. 1 .

FIGS. 4A-4D are schematic views of example control point resamplingtechniques.

FIGS. 5A-5C are schematic views of example energy layer reorganizationand re-distribution techniques.

FIGS. 6A-6C are schematic views of example energy layer reorganizationand re-distribution techniques.

FIGS. 7A-7C are schematic views of example energy layer reorganizationand re-distribution techniques.

FIGS. 8A and 8B are schematic views of an example of a spot deliverysequence re-organization and interpolation technique that may be usedbetween the control points.

FIG. 9 is a schematic view of an example computing device executing anysystems or methods described herein.

FIG. 10 is an example of a graphical user interface that illustratesplan selections for presenting to a user.

FIG. 11 is an example arrangement of operations for optimizing theoperation of the system of FIG. 1 .

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION

This description describes implementations and methods of particlespot-scanning therapy to deliver a particle beam in a substantiallycontinuous manner. In implementations, a particle beam is delivered in asubstantially continuous manner in connection with one or both of agantry, a couch, or other arrangements whereby an impact angle of theparticle beam is altered. Throughout the description hereof, the termproton is used as an example of a particle. It is to be appreciated thatthe invention hereof should not be so limited to a proton and theinventors recognize that the principles are applicable to all particlesand the terms are generally interchangeable in the context of thisdisclosure.

The inventors hereof have identified inefficiencies associated withconventional spot-scanning proton therapy (e.g., Intensity ModulatedProton Therapy (IMTP) and the like). Namely, conventional techniques maynot maximize an effectively continuous proton delivery. For example, andperhaps among other things, such conventional systems may effectivelycease proton delivery when changing between impact angles (definedbelow) with reference to a desired target (defined below) (a protonmachine will typically first adjust the impact angle and then deliverthe proton beam and thereby limit treatment efficiency). In addition,stopping and starting of the proton delivery can impact the calibrationof the proton delivery system as some systems experience a vibrationwhen switching between delivery and non-delivery.

With reference now to the Figures, FIG. 1 illustrates an implementationof a proton delivery system 100 for delivering a substantiallycontinuous proton treatment to a patient using one or more arctrajectories or non-iso centric movement with couch and gantry at sametime. As illustrated, system 100 includes a particle accelerator 102that delivers one or more proton beams 104, via a beam line 106, tooutput 120. Particle accelerator 102 accelerates charged particles andarranges charged particles in well-defined beams before extractingproton beams 104 via beam line 106. Examples of particle acceleratorsinclude colliders, cyclotrons, synchrotrons, laser proton accelerators,and the like.

In some implementations, accelerator 102 may be positioned remote fromoutput 120 such that accelerator 102 may be centrally located andselectively connectable to multiple outputs 120.

With continued reference to FIG. 1 , beam 104 is output 120 towards adesired target of a body 114 having an isocenter or multiple continuousmoving isocenters 116 located on a treatment station 110. Beam isdirected at the desired target with respect to an impact angle (IA)relative to the current isocenter 116, which will be described inadditional detail below. For ease of disclosure, body 114 may bereferenced herein as a tumor, and treatment station 110 will bereferenced as a couch or a table, yet it is to be appreciated that othertargets and treatment stations are contemplated and the disclosureshould not be limited to the examples.

Impact angle as used in this disclosure is the angle in which body 114experiences beam 104 and is shown as the angle between a plane thatextends through the isocenter 116 of body 114 (parallel with treatmentstation 110) and beam 104. For ease of disclosure, each change in impactangle IA is the result of movement of one or both of (i) adjustment ofoutput 120 about a gantry 122 among a plurality of control points 124(as shown in the Figures) with respect to body 114, (ii) adjustment ofbody 114 at a plurality of control points 124 with respect to output120, via movement of the treatment station 110 or the like, (iii)movement of output 120 and movement of body 114, each with respect toone another independently or simultaneously, or (iv) other suitablemeans.

In an implementation, magnets (not shown) may be provided in or aboutoutput 120 and/or along the beam line 104 that have adjustable currentsto selectively adjust beam 104. In an implementation, a degrader orenergy selection system (such as a wedge or the like) in the beamlinemay be provided to offer a selectively adjustable proton energy; forexample, different energies may be more desirable based on anappropriate treatment. A range shifter (sometimes referred to as abolus) (not shown) may be used (e.g., in the gantry nozzle) to attenuatethe proton energy. A proton multi-leaf collimation system may be used tosharp the proton spot lateral penumbra during the delivery and rotationof the gantry, couch, or the like. Such an attenuation and selection maybe utilized to alter the energy of the proton beam and achieve desireddepths of treatment. For purpose of this disclosure, different depthswithin body 114 will be referenced as energy layers. Multiple energylayers can be used in the system 110 described herein to effectivelytreat a three-dimensional tumor 114.

In some examples, range shifter may be used to degrade, or broaden, beam104. During a session, range shifter may move continuously during thegantry rotation with respect to isocenter 116. Range shifter may be usedto optimize an air gap between the range shifter and the patient's skinto ensure the proton beam 104 reaches a designated position 116 that hasa pre-defined size and is generally associated with tumor 114.

In some arrangements, system 100 may be deployed in conjunction with aconfiguration system, such as, for example, an imaging system. Inimplementations, the configuration system may be a Cone-Beam ComputedTomography (CBCT), a Fluoroscope, stereotactic imaging system, surfacematching camera system, or other similar devices that can monitor thepatient during the proton beam delivery.

In an implementation, system 100 delivers a substantially continuousbeam of protons 104 during a session and throughout any changes to oneor both of the impact angle (IA)—whether such impact angle is alteredvia changing control points via (i) movement of output 120 along agantry G, (ii) movement of couch; or (iii) a combination thereof. Forease of disclosure, the remainder of this disclosure will discloseembodiments where impact angle is altered by movement of output 120about control points 124 along a gantry G but it is equally contemplatedthat IA may be otherwise altered and the scope of this disclosure shouldnot be limited to the disclosed embodiments.

As described in more detail below, system 100 may be generallyconfigured to minimize a number of energy layers for one, some, or allof the beams that may be directed toward one or more control points. Toachieve such minimization, in an implementation, system 100 mayundertake one or more of the following steps (i) filters lower weightedenergy layers from the session, (ii) filters lower weighted protonspots, and (iii) re-arranges the remaining energy layers and/or protonspots between two or more consecutive control points which therebymaintains the same robust plan quality and is formatted to yield asubstantially continuous step-and-shoot proton arc delivery.

A session will now be described. In an implementation, output 120 emitsbeam 104 towards a desired location of tumor 114 of patient 112 at anumber of impact angles IA; accordingly, control points 124. As anexample, output 120 moves about a track 122 in a manner that facilitatesthe proton beam 104 to reach target 114 about different impact angles IAand at a number of associated control points 124. For example, system100 may be configured to provide an optimized session in accordance witha cancer treatment session.

In implementations, system 100 provides session algorithms and platforms(i.e., executing on the data processing hardware 130) to deliver anoptimized session to patient 112 via output 120. In an implementation,system 100 maximizes one or more sessions through: (a) determining anoptimized number of energy layers per control point (e.g., 1-6 energylayers), (b) determining an optimum number of control points (and/orimpact angles IA), and (c) performing spot beam weighting andpositioning at each control point 124 (and/or imaging angle). In otherwords, system 100 provides a session that optimizes the number, andposition, of control points 124 (and/or impact angles) and identifiesthe weight and position of each beam reaching a desired location abouttumor 114.

In an implementation, system 100 may include one or more substantiallycontinuous scanning modes, such as, but not limited to a step-and-shootspot scanning mode, a continuous arc delivery mode, or other deliverymodes. In a step-and-shoot spot scanning mode, system 100 switchesenergy layers of beam 104 by selecting different energy layers (e.g., byusing degrader and adjusting magnets as described above) to direct beam104 to the desired positions about target 114 among various impactangles (IA) and while the imaging angle (IA) is adjusting (e.g., whilegantry is rotating between control points 124).

In implementations, system 100 delivers one or more beams 104 at eachcontrol point 124 (and/or impact angle (IA)) and, in some embodiments,each proton beam 104 may be associated with an energy layer. In animplementation, each energy layer may be different and, in otherimplementations some or all of the energy layers may be common.

In some implementations, a step-and-shoot approach may save time byswitching among one or more energy layers while adjusting impact anglesIA. In other words, system 100 can change the one or more beams 104between one or more energy layers during the IA adjustment, therebyresulting in a reduced overall session time. For example, considering agantry rotation speed of three degrees per second, during the one secondit may take gantry to rotate to a next control point three degrees away,system 100 may change the one or more energy layers in which beam 104 isdirected.

As earlier described, in some examples, at each control point, beam 104may include 1-6 energy layers. In an exemplary session, each controlpoint 124 may include a beam 104 having one energy layer. In alternativesessions, it may be desired to reach one or more energy layers for oneor more control points 124—in which case, system 100 changes energylayers without gantry rotation at such control points 124. As a result,system 100 may provide multiple beams 104, each having a differentenergy layers. Both scenarios result in full or partial tumor 114(three-dimensional) coverage from multiple impact angles IA and/orcontrol points 124 thereby providing a system that delivers a full androbust tumor coverage dose through one or more arc trajectories.

In a continuous arc delivery mode, instead of delivering the proton beam104 at each static control point 124, system 100 continuously deliversthe proton beam 104 while changing the impact angle IA or control points124. So instead of utilizing discrete control points 124 or impactangles IA that are described above for the step and shoot examples, in acontinuous arc delivery mode, system 100 considers each control point124 or impact angle IA as being within an angular range (e.g.,−0.5<α<0.5 degrees) or a position range (e.g., −1 mm<x<+1 mm couchposition) which will be referred to herein as a control point samplingfrequency (CPSF). It will be appreciated, therefore, that a highercontrol point sampling frequency indicates a smaller angle or positionspread between adjacent control points. As a result, such discreteradiation delivery through step & shoot mode through different controlpoints will be a close approximation to the continuous radiationdelivery with such control point range (e.g., from −10 degree to 20degree partial arc and/or −10 cm to +20 cm couch movements).

In implementations, and as described in the examples that follow,control point sampling frequency (CPSF) may be introduced to effectivelyminimize the dosimetric difference witnessed upon body 114 as comparedwith the previously described step-and-shoot delivery. For example, thedesired control point sampling frequency of the continuous session maysuggest that four degrees between each effective control point (e.g.,from 0 to 4 degrees) is substantially dosimetrically equal to a step andshoot session delivered at 2 degrees.

In an implementation, there is almost no dosimetric difference betweenstatically delivering the proton beam 104 at one degree (step-and-shootmode) and dynamically delivering the proton beam from 0.5 to 1.5 degree(continuous arc delivery mode). But in the latter continuous case,delivering proton beam 104 continuously during the gantry rotationhaving a CPSF of 1 degree—additional time may be saved and gantryinertia, vibrations during stop and start or other mechanical issues canbe avoided.

In some implementations, system 100 may be configured to determine anoptimized number of control points 124 and re-sampling the controlpoints 124 to achieve a desired control point sampling frequency CPSF.In addition, system 100 may be configured to filter energy layersassociated with each control point 124 such that the energy layers areweighted and those having low monitor units (MU) are removed. In someimplementations, system 100 may also be configured to organize andallocated energy layers to nearby control points 124 instead of, priorto, or after filtering the energy layers.

To improve the calculation and optimization speed, system 100 may employa progressive dose grid sampling method, which may be defined by theunit of energy deposition in the Computer Tomography (CT) set or patientbody. For example, one (1) cubic center size cube consists of 1000 dosegrids with 1 mm×1 mm×1 mm size or 1 dose grid with 10 mm×10 mm×10 mmsize. An implementation of such a progressive dose grid sampling methodmay utilize a coarse dose grid size, and then progressively reduce thedose grid size during the optimization.

System 100 includes memory hardware 132 in communication with the dataprocessing hardware 130. The memory hardware 132 stores instructionsthat when executed on the data processing hardware 130 cause the dataprocessing hardware 132 to perform operations, such as the methoddescribed with respect to FIG. 2 , the method described with respect toFIGS. 3A-3C, or the method described with respect to FIG. 3D.

FIG. 2 describes an example arrangement of operations for a method 200of operating system 100. At block 210, system 100 pre-defines a protonarc range (i.e., an initial angle α_(i) and a stop angle α_(s))associated with gantry opening 122 and/or the rotation of table 110 orcouch/table translation movement (i.e., an initial position x₁ and astop position x_(s)). In some examples, a user defines the proton arcrange. For example, system 100 sets an initial angle α_(i) or positionof control point 124 (i.e., gantry location) of proton output 120emitting beam 104. In some examples, system 100 sets the initial angleα_(i) at 10 degrees and an initial stop angle α_(s) at 60 degrees. Insome examples, if the gantry is capable of rotating at 360 degrees, thenthe initial angle α_(i) is set at zero degrees and the initial stopangle α_(s) is set at 360 degrees. Other values of the initial anglesα_(i) and stop angles α_(s) are possible as well. In someimplementations, since table/couch 110 is capable of translationalmovement, system 100 also sets a table/couch initial angle and positionfor the table/couch 110 with respect to proton output 120. In this case,control point 124 is defined as table translation and rotationalmovement, for example every one centimeter, every two centimeters, orother every one degree, every two degree as well. Therefore, in someimplementations, system 100 defines an initial angle α_(i) for output120 and/or an initial angle for the table/couch 110 resulting in therotation of one or both of the proton output 120 and the table/couch110. As a result, system 100 considers the rotation of one or both ofthe output 120 and the table/couch 110 with respect to one another togenerate the proton arc range.

At block 220, system 100 determines a coarse control point samplingfrequency CPSF as shown in FIG. 4A. In other words, system 100identifies a number of gantry locations or control points 124. As shownin FIG. 4A, system 100 identifies eight control points 124 within thegantry's 360-degrees of freedom. Other number of coarse control pointsmay be used as well.

Referring back to FIG. 2 , at block 230 system 100 determines anoptimization treatment plan for patient 112. The optimization treatmentplan determines a beam dose plan used by the beam 104 to irradiate body114 as well as spare nearby tissue. In some examples, system 100considers the anatomy of patient 112, and determines a beam energy(i.e., energy layer), a beam spot position, and a number of protons tobe delivered in each beam 104 to patient 112. In addition, system 100optimizes a dose distribution in patient 112 (for example, robustnessoptimization by considering daily treatment setup and proton rangeuncertainties; radiobiology effect (RBE) optimization by consideringradiation biology effect of the proton beam), which allows a robust dosedistribution or biological effective dose to body 114 as well as sparethe healthy tissue and organs under these uncertainties. In someimplementations, system 100 determines the effects of potential changesto body 114, for example, and adjusts the treatment plan accordingly,which may be referred to as treatment plan adaptation. Some changes mayinclude, the patient gaining or losing weight, the tumor changing size,or other considerations. By using robust optimization, system 100 iscapable of providing optimal robust target coverage while sparinghealthy tissue.

At block 240, system 100 may first (A) optionally optimize the samplingfrequency of the control points 124 (e.g., iteratively increasingcontrol points numbers at block 240), the energy layer(s) and protonspots associated with each control point 124 to optimize the deliveryefficiency for delivering proton beam 104, resulting in an optimizedtreatment plan. In other words, system 100 uses a random iterativeprocess that selects the optimized energy layers and spot position andweightings of beam 104 for the treatment.

Following the optional control point, energy layer optimization and spotdelivery sequence optimization described about in block 240 or, insteadskipping the optional optimization about (A), system 100 may beformatted to either:

-   -   (i) Re-sample the control points 124, and re-organize and        re-distribute the energy layers between the control points 124        as illustrated in FIG. 2A. For example, sequential optimization        can be utilized such that system 100 first increases the control        point sampling frequency CPSF through control point re-sampling        and energy layer re-distribution mechanism (as described above)        and then employs one or both of energy layer filtration and spot        number reduction mechanisms to reduce the number of energy        layers and spot number per plan (as described above), and vice        versa; or    -   (ii) Allow the practitioner to identify a pre-defined control        point sampling frequency (CPSF) and, based on the desired CPSF,        pre-defining energy layers and performing sorting as illustrated        in FIG. 2B. For example, if a practitioner defines a prostate        proton arc plan having two degrees per control point (i.e., 180        control points on a 360 degree rotation axis), in order to        optimize the arc plan in a reasonable calculation time and        computer resources, system 100 predefines control point zones        (e.g., in the prostate example, we defined 8 zones and each zone        contains 20 control points). In such an example, each zone        contains a range of the energy layers (e.g. 250 MeV to 70 MeV)        and each control point and then assigned with a sub-predefined        range e.g. control point #1 range from 250 MeV to 210 MeV,        Control point #2 range from 210 MeV to 160 MeV. It can be evenly        distributed or unevenly distributed. Then it followed by        optimization process in which it will find an optimum plan        quality based on such predefined zone and control points.

In an implementation, at block 250 system 100 generates an optimizedplan for patient 112. As described, the determined plan may be tailoredto accommodate a specific patient 112, and may be adjustable based onvariables of patient 112 (e.g., the patient's daily treatment setup,proton range uncertainties, tumor motion, weight, the size of the tumor114, other patient related measurements, and the like). In animplementation, system 100 includes a processor that is additionallyprogrammed to generate, and identify, one or more alternative plans thatmay alternatively account for different plan parameters, plan qualities,delivery efficiencies, clinician defined variables, and the like). In animplementation, a user is able to pick one from the one or more numberof plans. In an implementation, a database may be provided that includeshundreds of plans which could be based on the objective value of eachplan, each individual objective function or delivery time for differentmachines (fix gantry, full gantry, synchrotron or cyclotron machine) orparameters (energy layer numbers, spot numbers or MU). An examplegraphical user interface is provided at FIG. 10 that illustrates anumber of such plans in which the clinician could select.

FIGS. 3A and 3B describe a more detailed example arrangement ofoperations than FIG. 2 for a method 300 of operating system 100. Atblock 310, similar to block 210 of FIG. 2 , system 100 defines a protonarc range (i.e., the initial angle α_(i) and stop angle α_(s) of output120 within the rotation of gantry and/or couch 110 (see FIG. 4A)). Forexample, system 100 sets an initial angle α_(i) as the initial controlpoint 124 (i.e., gantry location) of output 120 emitting beam 104. Insome implementations, system 100 also sets a table initial angle for thetable 110. Therefore, in some implementations, system 100 defines aninitial angle α_(i) for output 120 and/or a table initial angle fortable 110, such that beam 104 from output 120 is capable of reachingtumor 114 at the desired impact angle IA. The operating system coulddeliver multi-iso center particle beam therapy or non-coplanarmulti-isocenter particle beam with couch/table and gantry movements.

At block 320, similar to block 220 of FIG. 2 , system 100 determines acoarse control point sampling, as shown in FIG. 4A, between theidentified initial angle α_(i) and the identified initial stop angleα_(s). In other words, system 100 identifies a set of gantry and/orcouch locations or control points 124 between the identified initialangle α_(i) and the identified initial stop angle α_(s). As shown inFIG. 4 , system 100 identifies eight control points 124 within thegantry's 360-degrees. In this example, the initial angle α_(i) is atzero degrees and the initial stop angle α_(s) is at 360 degrees. Othernumbers of sampling control points 124 between the initial angle α_(i)and the initial stop angle α_(s) are possible as well.

Referring back to FIGS. 3A and 3B, at block 330, system 100 determinesan optimization treatment plan for patient 112 that determines a beamdose plan used by beam 104 at the identified control points 124(identified at block 320) to irradiate tumor 114 similar to theoptimization treatment plan described above with respect to block 230 ofFIG. 2 . In some examples, data processing hardware 130 executes theoptimization of the treatment plan based on information stored on thememory hardware in communication with the data processing hardware 130.The optimization may include one or more optimization techniques ormethods, such as but not limited to, robust optimization, four orfive-dimensional (time and geometry change or frequency dimension)robust optimization, adaptive optimization, and radiation biologicaleffect (RBE) optimization. The optimization treatment plan includesidentifying an energy layer associated with a beam, a spot position, anda number of protons to be delivered in each beam 104 originating fromoutput 120 at the identified control points 124 (identified at block320). In addition, system 100 determines the optimization treatment planfor patient 112 at the identified control points 124 by considering theanatomy of the patient 112. In addition, system 100 optimizes a dosedistribution in patient 112 (for example, by considering daily treatmentsetup and proton range uncertainties), which allows a robust dosedistribution to the tumor as well as spare the healthy tissue and organsunder these uncertainties. In some implementations, system 100determines the effects of potential changes to tumor 114, for example,and adjusts the treatment plan accordingly, which may be referred to astreatment plan adaptation. Some changes may include the patient gainingor losing weight, the tumor changing size, or other considerations. Byusing robust optimization, system 100 is capable of providing optimalrobust target coverage while sparing healthy tissue.

At block 340, system 100 optionally randomly selects between an energyfiltration method at block 342, 342A and a control point re-sampling,energy layer re-distribution method and spot delivery sequencere-distribution at block 344. At optional block 342A, system 100 filtersthe energy layers of beam 104. In other words, system 100 removeslow-weighted energy layers associated with one beam or the total beamsassociated with the treatment plan. System 100 may define a cut off MUweighting threshold for one or both of the energy layers or the spotnumbers, so that the energy layers or spot numbers fail to meet thecutoff threshold will not be further considered at later steps of themethod. For example, system 100 identifies the lowest 10% of MUweighting energy layers associated with all control points 124 andremoves the identified lowest 10% of energy layers associated with allthe control points 124. Other cutoff percentages may be used as well. Inother examples, the MU weighting threshold for the energy layer may beassociated with beams 104 outputted at each control point.

As previously discussed, at block 340, system 100 may either (i)randomly select between the energy filtration method at block 342A andthe control point re-sampling, energy layer re-distribution and spotdelivery sequence re-organization method at block 344 (as illustrated inFIGS. 3A-3C) or (ii) allow the practitioner to identify a pre-definedcontrol point sampling frequency (CPSF) and, based on the desired CPSF,pre-defines energy layers and perform sorting based on the particulartreatment (as illustrated in FIG. 3D).

If system 100 selects the control point re-sampling, energy layerre-distribution and spot delivery sequence method at block 344, thensystem 100 filters one or both of the energy layers or the spotsassociated with beams 104 of the treatment plan.

In an implementation, system 100 re-samples the control points 124, ormore specifically increases the number of control points as shown inFIGS. 4B and 4C. FIG. 4B illustrates a method that system 100 uses tosplit a control point 124 into first and second control points 124 (1 aand 1 b), while FIG. 4C shows a method used by system 100 to add acontrol point 124 (e.g., adding control point 2). While certainsplitting methods are disclosed, other splitting methods may be employedand the disclosure should not be so limited.

FIG. 4B illustrates an implementation of a first control point 1, 124split into two new control points 1 a, 1 b, 124, each having a positiondifferent from the position of the first control point 1, 124, forexample, adjacent to the first control point 1, 124, such as, on eitherside of the first control point 1, 124. In some examples, the firstcontrol point 1, 124 may be split into more than two control points 124,e.g., three or more. Additionally, referring to FIGS. 5A-5C, the energylayer(s) (EL) associated with a control point 124 are re-distributed andre-organized. For example, the first control point 1, 124 is capable ofemitting beams 104, where each beam has an energy layer EL from theenergy layers EL1-ELn. Each energy layer EL1-ELn is optimized to delivera robust proton treatment therapy to the patient 112 and ensure a robusttumor coverage as well as sparing organs that are not cancerous. In someexamples, the energy layers EL1-ELn are arranged in ascending/descendingorder where the first energy layer EL1 associated with a first beam 104has less energy than the last energy layer ELn associated with adifferent beam 104. In other words, the different beam 104 having thelast energy layer ELn (highest energy layer) reaches the furthestdistance within the tumor 114. The first control point 1, 124 is splitbetween a first new control point 1 a, 124 and a second new controlpoint 1 b, 124. As shown, system 100 splits the energy layers EL1-ELn ofthe first control point 1, 124 by consecutively giving each one of thefirst and second new control points 1 a, 1 b, 124 energy layers EL1-ELnof the first control point 1, 124. Therefore, once all the energy layersEL1-ELn of the first control point 1, 124 are split between the firstand second new control points 1 a, 1 b, 124, then the first new controlpoint 1 a, 124 has a number of energy layers N_(EL(1a)) calculatedaccording to:N _(EL(1a))=(N+1)/2 if N is odd  (1A)N _(EL(1a)) =N/2 if N is even  (1B)where N is the total number of energy layers EL of the control point 1,124 prior to being split. In addition, the second new control point 1 b,124 has a number of energy layers N_(EL(1b)) calculated according to:N _(EL(1b))=(N−1)/2 if N is odd  (2A)N _(EL(1b)) =N/2 if N is even  (2B)

In some implementations, an MU associated with a beam at the firstcontrol point beam 1, 124 for a specific energy layer i may bedetermined by:Beam 1old=Σ_(N) ¹oldMUweighting(i)*EnergyLayer(i)  (3)where i is an energy layer EL, and N is the total number of energylayers.

After splitting the first control point 1, 124, each of the first andsecond new control points 124 has a beam energy calculated based on thefollowing equations when N is even:Beam 1a=Σ _(i=0) ^(N/2−1)oldMUweighting(2i+1)*EnergyLayer(2i+1)  (4A)Beam 1b=Σ _(i=) ^(N/2)oldMUweighting(2i)*EnergyLayer(2i)  (5A)The beam energy for the first and second new control point 124 may becalculated based on the following equations when N is odd:Beam 1a=Σ _(i=0) ^((N−1)/2)oldMUweighting(2n+1)*EnergyLayer(2i+1)  (5A)Beam 1B=Σ _(i=0) ^((N−1)/2)oldMUweighting(2n)*EnergyLayer(2i+1)  (5B)where N is the total number of the energy layer.

In an implementation, system 100 employs a spot number (weighting)mechanism in addition to, or separately from, the energy layerfiltration as described above. The spot number or weighting reductionmechanism may be utilized to filter, or otherwise remove, the MU spotsor lines sequentially designated as being below a certain threshold. Itis to be appreciated that this filtration may occur simultaneously, orrandomly, during the optimizations. In exemplary implementations, thethreshold may be determined as a bottom ten percent (10%) after energylayer filtration, integrated with energy layer filtration, orindependent of energy layer filtration.

In an implementation, system 100 may be designed to undertake energylayer re-connection to reduce or increase the number of energy layersand the associated switching time. For example, and among others, system100 adjusts the energy layer from a first beam impact angle (IA) to thesame energy level when an adjacent impact angle (IA) has (i) an energydifference that is below a threshold level, and (ii) a comparable MUweighting. For example, consider 115 MeV and 10 MU when the first impactangle (IA) is 0 degrees, and 110 MeV and 5 MU when the adjacent impactangle (IA) is 1 degree. In this instance, the energy layers of 110 MeVmay be adjusted to 115 MeV so the system reduced one (1) energy layerswitching time during the proton beam delivery.

FIGS. 6A-6C illustrate another example of splitting the energy layers124 associated with a control point 1, 124, which may include for eachenergy layer (EL), dividing the MU associated with that energy layer ELbetween a first and second new control point 1 a, 1 b, 124 based on athreshold MU (e.g., a fraction of the MU associated with the originalcontrol point 1, 124) associated with each one of the first and secondnew control points 1 a, 1 b, 124. For example, an energy layerEL₁-EL_(n) of a first control point 124 has a first MU value. The MUvalue may be split between the first new control point 1 a, 124 and thesecond new control point 1 b, 124, where each of the first and secondnew control points 1 a, 1 b, 124 is associated with a fraction f_(a),f_(b) of the MU value associated with the energy level EL₁-EL_(n). Thesummation of the fractions f_(a), f_(b) equals to one (f_(a)+f_(b)=1).In other words, the first new control point 1 a, 124 may have a firstfraction f_(a) of the energy layer EL₁-EL_(n) and the second new controlpoint 1 b may have a second fraction f_(b) of the energy layerEL₁-EL_(n). For example, the energy level EL₁-EL_(n) may have an MUvalue of 120 MU. After splitting the energy level EL₁-EL_(n) into thefirst new and second new energy levels 1 a, 1 b, 124, then theEL₁-EL_(n) energy level EL₁-EL_(n) may having a first fraction f_(a)being half the MU value of the MU of the energy layer EL₁-EL_(n), whilethe second new control point 1 b, 124 has an energy layer having theremaining half of the MU value of the MU of the energy level EL₁-EL_(n).As such, the total number of energy layers of the first and second newcontrol points 1 a, 1 b, 124 are doubled; however, the total MU of thefirst and second new control points 1 a, 1 b, 124 is equal to the MUassociated with the old control point 1, 124. Therefore, if the energylevel EL₁-EL_(n) has an total MU of 120 MU, then the first control point1 a, 124 may have an MU of 60 MU and the second control point 1 b, 124has an MU of 60 MU. If the energy level EL₁-EL_(n) has an MU of 120 MU,then the first control point 1 a, 124 may have an MU of 40 MU (wheref_(a) is ⅓) and the second control point 1 b, 124 has an MU of 80 MU(where f_(a) is ⅔). In another implementation, the system may use acombination of both the energy split such as re-distribution togetherwith employing a split of the MU weighting of each energy mechanism.

Referring back to FIGS. 4C and 7A-7C, in some implementations, a secondcontrol point is added in addition to an original first control point,where the first control point 1, 124 remains in the same location andthe second control point 2, 124 has an adjacent location to the firstcontrol point 1, 124. In some examples, more than one control point 124is added to the first control point 1, 124, e.g., a third or morecontrol points may be added. Referring to FIG. 4C, a second controlpoint 2, 124 is added in addition to the first control point 1, 124.FIGS. 7A-7C illustrate the energy layer EL₁-EL_(n) re-organization andre-distribution process. FIG. 7A illustrates an original first controlpoint 1, 124 that includes energy layers EL₁-EL_(n). In this case,system 100 adds a second control point 2, 124, which consecutively takesevery other energy layer EL₁-EL_(n) from the first control point 1, 124,which results in a first new control point 1, 124 shown in FIG. 6B, andthe second control point 2, 124 shown in FIG. 7C. As a result, the newfirst control point 1, 124 (FIG. 7 ) has less energy layers EL₁-EL_(n)than the original control point shown in FIG. 7A. In addition, the firstnew control point has a number of energy layers calculated based onequation 1, while the second new control point 2, 124 has a number ofenergy layers calculated based on equation 2.

In some implementations, an MU associated with the first control pointbeam 1, 124 for a specific energy layer i may be determined by equation3 above. In addition, the new first control point 1 a, 124 and the addedcontrol point 2, 124 have a beam energy determined by the followingequations when N is even:Beam1new=Σ_(i=0N/2) ^(N/2−1)oldMUweighting(2i+1)*EnergyLayer(2i+1)  (6A)Beam 2=Σ_(i=1) ^(N/2)oldMUweighting(2i)*EnergyLayer(2i)  (7A)if N is odd:Beam 1new=Σ_(i=0) ^((N−1)/2)oldMUweighting(2i+1)*EnergyLayer(2i+1)  (6B)Beam 2=Σ_(i=1) ^((N−1)/2)oldMUweighting(2i)*EnergyLayer(2i)  (7B)where N is the total number of the energy layer.

As described in FIGS. 4B, 4C, 5A-5C, 6A-6C, and 7A-7C the energy layersEL1-ELn of a first control point 124 are split (FIGS. 4B, 5A-5C) orreduced (FIGS. 4C and 7A-7C), or its associated MU values are split(FIGS. 4B, 6A-6C) in a consecutive manner, more specifically splittingeach energy layer EL to one of the new consecutive points. However, theenergy layers EL associated with the first control point 1, 124 may besplit in other ways, such as, but not limited to, the MU associated witheach energy layer EL of the control point, a total value of MUs percontrol point, a total number of energy layers EL associated with eachcontrol point 124, or any other method.

It is emphasized that any method to re-organize and re-distribute theenergy layers EL may be used, that the number of energy layers may notbe maintained, i.e., one or more additional energy layers could be addedas a re-sampling mechanism in block 362. Similarly, in some examples,each energy level within a control point may be split differently thananother energy level within the same control point. In some examples,energy layer will go through a sorting process that higher energy layermoving to control point 1 and lower energy layers moving to controlpoint 2.

FIGS. 8A and 8B illustrate an exemplary implementation of a method toenhance a spot delivery sequence to thereby undertake a re-organizationand re-distribution of a first control point (e.g., having a gantryangle 40°). As illustrated in each of FIG. 8A and FIG. 8B one or morecontrol points 124 may be divided into two or more control points (e.g.,gantry angles of 39° and 40° or couch position of x=10 cm and 10.5 cm)wherein the resultant, divided control points each have a position organtry angle that is different from the position of the control pointfrom which the division occurred, 124. In an implementation, forexample, the divided control points may be positioned adjacent to thefirst control point, 124, such as, on either side of the first controlpoint, 124. In another example, the first control point, 124 may besplit into more than two control points 124, e.g., three or more. Inaddition to the energy layer re-distribution and re-organization such asFIGS. 5A-5C, each control point, 124, might contain multiple energylayers; such that each energy layer contains a layer of spots andfurther wherein each spot has a position in an X, Y direction (asreferenced from the beam eye view).

An example of a sequence re-organization and re-distribution will now bedescribed. In an implementation, the control point, 124 is capable ofemitting beams 104, wherein at least one of the emitting beams hasenergy layer(s) EL. In the described example, each energy layerEL₁-EL_(n) may be directed to one or more spots which may be, in apreferred form, optimized to deliver a robust proton treatment therapyto the patient 112 (e.g., to help ensure robust tumor coverage, spareorgans that are not cancerous, and the like). It is to be appreciatedthat the control point splitting can be used in a variety ofenvironments, including line scanning sequence particle therapy machinesas shown in FIG. 8A and spiral scanning sequence particle therapymachines as shown in FIG. 8B. In each of FIGS. 8A and 8B, the spots ofthe specific energy layer illustrated with respect to the first controlpoint, 124 are divided into first and second new control points based onthe machine delivery sequence. Accordingly, the radiation dose deliveredfrom control point that was divided is approximately equal to theaggregate radiation dose that is delivered by first and second controlpoints. The position or gantry angle of the first and second controlpoints is so close relative to first and second control point such thatthe proton beam delivered through the continuous arc deliveryapproximately equals the proton beam had it been delivered at theposition of the static control point from which first and second controlpoints were derived. Accordingly, the result of the describedre-distribution and re-organization from a primary control point dividedinto two or more sub-control point is interpolation of the energy andspot delivery sequence for a continuous and dynamic particle arctreatment. For clarity, the division of the control points may include,energy layers, spots, or a combination of energy layer and spot deliverysequence re-organization and re-distribution. And, for greater clarity,the foregoing re-organization and re-distribution technique may beincorporated at one or both of the treatment plan system to optimize theplan and in the hardware (e.g., by the gantry, beamline, cyclotron, orthe like) in each case to deliver an efficient and effective particlearc therapy.

In an implementation, blocks 342A and 344 may be implemented randomlyfor example, implementing block 342A one or more times than implementingblock 344 one or more times, or implementing block 344 one or more timesthan implementing block 342A one or more times. The two blocks 342A and344 are interchangeable and their interchangeability does not affect thetreatment plan. However, the interchangeability of the two blocks 342Aand 344 may affect the calculation time/speed for determining thetreatment plan. For example, when system 100 executes block 342A first,the system 100 filters or removes low-weighted energy layers in theplan, which results in less energy layers and spots compared to whensystem 100 re-samples the control points 124 at block 346 first. Moreenergy layers and spots take more time to calculate and optimize.Therefore, when system 100 executes block 344 before block 342A, itmight take the system 100 longer to find a plan than when the system 100executes block 342A before 344. For example, assuming there are eightcontrol points each having 50 energy layers and 1500 spots, then ifsystem 100 executes block 342A first, the result will remain eightcontrol points 124 with 40 energy layers and 1200 spots, which is lessenergy layers and spots than the original plan. Then system 100 executesblock 344 and re-samples the control points 124, where each controlpoint has less energy layers than the original control points prior tofiltration. However, if system 100 re-samples (block 344) the controlpoints 124 prior to filtration (block 342A, then system 100 has toperform calculations on a larger number of energy layers and spots,which increases the time to determine an optimization treatment plan.

In an alternative system, block 340 of FIG. 3A may be obviated andreplaced with a user pre-defined treatment plan identified by a user(See FIG. 3D). For example, a practitioner may identify a pre-definedcontrol point sampling frequency (CPSF) and, based on the desired CPSF,system 100 may process this information to pre-define energy layers andperforming sorting of the control points to identify a plan.

At block 350 (similar to block 330), system 100 determines an optimizedtreatment plan for patient 112 (e.g., a robust optimization or othertypes of optimizations described with respect to block 330). Theoptimization plan determines a beam dose plan for the beam 104 toirradiate the tumor 114. This optimization plan is based on the filteredenergy layers of block 342A or the random control point re-sampling andenergy layer re-organization and re-distribution at block 344.Therefore, the robust optimization at block 330 is different than therobust optimization of block 350, because each is based on the sample ofcontrol points 124 having different energy layers, e.g., theoptimization at block 330 is implemented on the control points 124having the identified energy layers, while the robust optimization atblock 350 is implemented on the control points 124 having the filteredenergy layers or resampled and reorganized energy layers or filteredspots.

As depicted and in some implementations, at block 360, system 100determines if the current plan quality is acceptable. Several methodsmay be used to determine if the plan quality is acceptable. For example,system 100 may determine if a current plan has reached target coverageor if an objective value is reached. For example, system 100 mayconsider a good quality plan to include a specific number of controlpoints 124 within the arc rotation. Therefore, an acceptable planquality may be identified when a plan has reached a threshold number ofcontrol points 124. In other examples, a good plan quality may beidentified when the plan has reached a specific proton beam deliverytime that a user has defined.

In some implementations, plan quality may be assigned an objective valuebased on one more factors associated with a plan and a plan may beidentified as a quality plan provided that the objective value is at orabove an identified threshold object value. For example, system 100 maydetermine if an objective value associated with the treatment plan hasincreased, e.g., by 10% from a previous objective plan and identifywhether this increase is acceptable.

In some examples, the previous objective value is an average of one ormore individual objective values. In an implementation, the objectivevalue may be a measurement of time for the cancer treatment plan to becompleted. The objective value may be other values as well. If theobjective value has not increased by a threshold value (e.g., 10%), thensystem 100 repeats blocks 340-356 until the objective value hasincreased by the threshold value. For example, the objective value maybe a measurement of time for the cancer treatment plan to be completed.The objective value may be other values as well. If the objective valuehas not increased by a threshold value (e.g., 10%), then system 100repeats blocks 340-360 until the objective value has increased by thethreshold value. The objective value may be determined based on anobjective function, also referred to as an optimization function andcost value, shown in the below equation:cost value(F)=w _(Target) *F _(Target) +w _(Risk1) *F _(Risk1) +w_(Risk2) *F _(Risk2)  (8)

Where w_(target) is a weight value associated with the target (i.e.,tumor), penalties value, or an importance factor, and F_(target) is thedifference between the current value vs. the goal that system 100 isaiming to reach, costlets, or indicators. w_(Risk1) is a weight valueassociated with the tissue or organs that are adjacent to the tumor; andF_(Risk1) is the difference between the current dose would be deliveredto the specific organs vs the goal that system 100 is aiming to sparefor this specific organs.

In some examples, F_(target) may be written as:F_(Target)=(D_(target)−D₀)² where D_(target) is the goal of prescriptiondose to the target and D₀ is the current dose to the target. The biggerdifference between the current value and objectives, the higher the costvalue is, which also means the system need to further optimize thetreatment plan to reach an optimized treatment plan.

In step-and-shoot mode, system 100 determines if the time for the gantryor couch rotation or translational movement, i.e., the rotation ofoutput 120 with respect to table 110 is greater than the time to switchenergy layers, then system 100 keeps at least one energy layer percontrol point 124, e.g., (1-6 energy layers per control point 124). Forexample, if it takes three seconds for gantry to move between twoconsecutive control points 124, and energy layer switching time is lessthan 3 seconds, then system 100 keeps at least one energy layer percontrol point 124. In an implementation of the continuous delivery mode,system 100 may optionally retain the control point resampling until itreaches a desired arc sampling frequency or process as set out in apre-defined manner (see, e.g., block 240 in FIG. 2 ).

As previously discussed, higher control point sampling frequencyindicates a smaller angle difference between the adjacent controlpoints. In this situation, delivery of a beam 104 simultaneously withthe gantry/couch rotation is a close approximation to delivering a beam104 at a static control point angle. Desired arc sampling frequencymeans that there is enough control points within an arc so that there isalmost no dosimetric difference between static step-and-shoot deliveryand continuous delivery mode. Reaching a desired arc sampling frequencymeans that to achieve enough sampling control point so there is minimumdosimetric difference between static step-and-shoot deliveries andcontinuous proton beam arc delivery.

In a system 100 that utilizes an iterative optimization approach basedon the random control point re-sampling, energy layer, spot deliverysequence re-organization, re-distribution, and energy layer filtrationand spot number reduction. During the random iterative optimizationprocess, each step may be arranged to generate a plan with an objectivevalue. And once the objective value has exceeded a pre-defined thresholdvalue, system 100 may reject the previous step and restarts the randomprocess again. In some implementations, the optimization processincludes, but is not limited to, radiobiology (RBE) optimization,physical dose optimization, and the like. For example, as illustrated inFIG. 3B, after system 100 filters the energy layers at block 342A orre-samples the control points 124 and re-organizes and re-distributesthe energy layers at block 344, 350A, if the objective value is higherthan 10% of the previous plan, the current filtered or re-sampled newcontrol points will be rejected and the system 100 starts a new randomsearch procedure based on the previous plan. If the objective value islower than the previous plan, system 100 accepts the new filtered orre-sampled control points 124 and continues the random search based onthe current plan.

At block 370, an implementation of a system 100 may determine if atreatment plan has reached a user defined quality based on userpreference, such as, e.g., a specific time, tumor coverage, or othermeasurable variables. If system 100 determines that the treatment planhas not reached the user defined quality, then system 100 reiteratesblock 340, described above by selecting a random method between theenergy layer filtration at block 342A or the control point re-samplingand energy layer re-distribution at block 344. System 100 repeats thisprocess until system 100 determines that the treatment plan reached isaccording to the user defined plan quality. Once, system 100 determinesthat the treatment plan reached is according to the user defined planquality, system 100 can begin treatment of the tumor 114 according tothe plan. System 100 randomly repeats blocks 342A and 344 as long as thetreatment plan has not reached a user defined quality to increase orsplit the original coarse sampling control points (shown in FIG. 4A)into new and finite control points without causing unacceptable plan anddose calculation time, resulting in a step-and-shoot or a continuousdelivery arc plan with desired control point 124 sampling frequency.Therefore, system 100 seeks to create enough sampling control points ora sampling rate for a continuous arc delivery. This results in asignificantly reduced calculation time. For example, system 100 maydeliver a beam 104 having at least one energy layer (e.g., 1-6 energylayers each outputted at as a separate beam) at each control point 124,where system 100, after executing blocks 342A and 344 determines thatthe SPArc includes 360 degrees of full rotation about the patient, withcontrol points at every two degrees. In other words, system 100 deliversa beam 104 to the patient 112 at every two degrees or continuouslydelivers the beam during the gantry/couch rotation, delivering the mostefficient treatment plan.

Referring to FIG. 3B, in some implementations, system 100 performsadditional optional improvements to the treatment plan of FIG. 3A. Atblock 380, system 100 performs random energy layer re-sampling on thepreviously reached treatment plan at block 370. For example, system 100randomly adds additional energy layers to the treatment plan at randomcontrol points 124 (i.e., existing control points 124). System 100 mayadd an additional 10% energy layer to further optimize the treatmentplan.

At block 342B, system 100 performs energy layer filtration similar tothe energy layer filtration performed in block 342A. Thereafter, system100 may perform an optimization step at block 382 that is similar to theoptimization referenced at blocks 330 and 350. At block 384, system 100undertakes to determine if the treatment plan quality has improvedcompared to the last plan quality. If the system 100 identifies that thetreatment plan quality has improved, then system 100 determines that thetreatment plan quality may be further improved and performs block342B-384 until system 100 determines that the plan quality can no longerbe improved. When system 100 determines that the treatment plan qualitymay not be improved, system 100 determines that it is the desiredtreatment plan for the patient 112.

In some examples, the desired treatment plan may be based on userpre-defined factors. Referring to FIG. 3C, at block 390, system 100delivers an optimized and efficient cancer treatment plan withcontinuous beam delivery, based on one of the user preferences. Forexample, some clinicians prefer best plan quality, so they will choosethe lowest objective value plan. Some clinicians prefer a fasterdelivery plan, so they might choose a plan with the shortest deliverytime while compromise the plan quality. Or some clinicians will choose amoderated plan with both good plan quality as well as medium deliverytime.

Traditional proton systems extract each energy layer one by one throughan energy selection method. However, system 100 includes a proton systemthat will be able to extract multi-energy layers at same time. In thiscase, system 100 delivers a proton beam 104 having multi-energy layersat a control point 124 in a step-and-shoot or continuously withoutcosting additional energy layer switch time. In energy re-distributionmechanism 344, system 100 use the methods described in FIGS. 2-6 tore-distribute the energy layer to the new control points 124.

FIG. 10 is a schematic view of an example computing device 800 that maybe used to implement the systems and methods described in this document.The computing device 800 is intended to represent various forms ofdigital computers, such as laptops, desktops, workstations, personaldigital assistants, servers, blade servers, mainframes, and otherappropriate computers. The components shown here, their connections andrelationships, and their functions, are meant to be exemplary only, andare not meant to limit implementations of the inventions describedand/or claimed in this document.

The treatment planning and delivery mechanism includes computationalbased optimal beam angle (step and shoot) and optimal arc trajectory(continues arc) searching software platform and delivery framework toimprove overall treatment plan quality and delivery efficiency. Theoptimal beam angle and trajectory-searching algorithm utilizes theentire solid angle search space for treatment dose optimization tofurther increase the therapeutic ratio. Optimal arc trajectories aregenerated and selected based on global optimization of the spotpositions, spot weighting, and beam angles. The most efficient arctrajectories are selected for treatment delivery.

The computing device 800 includes a processor 130, 810, memory 820, astorage device 132, 830, a high-speed interface/controller 840connecting to the memory 820 and high-speed expansion ports 850, and alow speed interface/controller 860 connecting to low speed bus 870 andstorage device 830. Each of the components 810, 820, 830, 840, 850, and860, are interconnected using various busses, and may be mounted on acommon motherboard or in other manners as appropriate. The processor 810can process instructions for execution within the computing device 800,including instructions stored in the memory 820 or on the storage device830 to display graphical information for a graphical user interface(GUI) on an external input/output device, such as display 880 coupled tohigh speed interface 840. In other implementations, multiple processorsand/or multiple buses may be used, as appropriate, along with multiplememories and types of memory. Also, multiple computing devices 800 maybe connected, with each device providing portions of the necessaryoperations (e.g., as a server bank, a group of blade servers, or amulti-processor system).

The memory 820 stores information non-transitorily within the computingdevice 800. The memory 820 may be a computer-readable medium, a volatilememory unit(s), or non-volatile memory unit(s). The non-transitorymemory 820 may be physical devices used to store programs (e.g.,sequences of instructions) or data (e.g., program state information) ona temporary or permanent basis for use by the computing device 800.Examples of non-volatile memory include, but are not limited to, flashmemory and read-only memory (ROM)/programmable read-only memory(PROM)/erasable programmable read-only memory (EPROM)/electronicallyerasable programmable read-only memory (EEPROM) (e.g., typically usedfor firmware, such as boot programs). Examples of volatile memoryinclude, but are not limited to, random access memory (RAM), dynamicrandom access memory (DRAM), static random access memory (SRAM), phasechange memory (PCM) as well as disks or tapes.

The storage device 830 is capable of providing mass storage for thecomputing device 800. In some implementations, the storage device 830 isa computer-readable medium. In various different implementations, thestorage device 830 may be a floppy disk device, a hard disk device, anoptical disk device, or a tape device, a flash memory or other similarsolid state memory device, or an array of devices, including devices ina storage area network or other configurations. In additionalimplementations, a computer program product is tangibly embodied in aninformation carrier. The computer program product contains instructionsthat, when executed, perform one or more methods, such as thosedescribed above. The information carrier is a computer- ormachine-readable medium, such as the memory 820, the storage device 830,or memory on processor 810.

The high speed controller 840 manages bandwidth-intensive operations forthe computing device 800, while the low speed controller 860 manageslower bandwidth-intensive operations. Such allocation of duties isexemplary only. In some implementations, the high-speed controller 840is coupled to the memory 820, the display 880 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 850,which may accept various expansion cards (not shown). In someimplementations, the low-speed controller 860 is coupled to the storagedevice 830 and low-speed expansion port 870. The low-speed expansionport 870, which may include various communication ports (e.g., USB,Bluetooth, Ethernet, wireless Ethernet), may be coupled to one or moreinput/output devices, such as a keyboard, a pointing device, a scanner,or a networking device, such as a switch or router, e.g., through anetwork adapter.

The computing device 800 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 800 a or multiple times in a group of such servers 800a, as a laptop computer 800 b, or as part of a rack server system 800 c.

FIG. 11 describes an example arrangement of operations for a method 400of optimizing the operation of system 100. In some implementations, themethod 400 may improve the mechanical connection between the controlpoints 124 based on the rotation speed and acceleration of the gantry122. In some implementations, the method 400 may quantitatively controlthe rotation speed and/or momentum changes of the gantry 122 duringdirect mechanical parameter incorporated iterative SPArc optimization(SPArc-MPO).

At block 402, coarse sampling is conducted. At block 404, an initialcoarse optimization is performed to calculate and place the energydistribution, spot position, and weighting in the coarse sampling arcsegments. At block 406, one or more control point may be re-sampled froma coarse frequency (e.g. 20 degree per control point) to a fine arcsampling frequency (e.g. 2 degree per control point) through theredistribution of the MU weighting, energy layers and spot number. In animplementation, and as shown on FIG. 11 , this may occur throughoptimization of an energy layer delivery sequence sorting andredistribution mechanism (referred to as the SPArc-seq) between thecontrol points 124, the arc sampling frequency, e.g., 1, 2, 2.5 degrees,etc., is determined.

At block 408, a machine-specific delivery sequence model (DSM_(arc)) maybe employed to calculate the delivery time and sequence based on machinespecific or proton system properties (including parameters and userdefined or preferred parameters). The machine specific parameters mayinclude mechanical parameters and electronic parameters. The mechanicalparameters may include (i) a delivery tolerance window, e.g., +/−1degree, (ii) gantry mechanical limitations, e.g., rotation speed,momentum changes, acceleration/deceleration speed, and (iii) mechanismof control point connections including a delivery window buffer andacceleration/deceleration speed to connect the adjacent control points.

Optimization of dose-volume objectives may be constrained by themachine-specific parameters that are calculated from themachine-specific delivery sequence model such as gantry speedlimitation, gantry acceleration, energy switch time, and spot deliverytime, where, in some implementations, the constraints may be defined asfollows. The division of control point resampling frequency may bedefined by the following equation:θ=α+β  (9)

Where θ is the angle resampling frequency, β is the tolerance window forideal delivery, and α is an angle interval which is used for energyswitching and gantry velocity acceleration. Gantry speed may be definedas:v _(i) *t _(i)≤β  (10)

Where v_(i) is the gantry velocity during delivery and t_(i) is the spotdelivery time within the i-th control point, respectively. Theconstraint of gantry speed difference between the neighbor controlpoints includes the constraint of rotation angle interval foracceleration and the constraint of accelerated time. Rotation angleinterval for acceleration may be defined as:(v _(i) ² −v _(i+1) ²)=2*sign(v _(i) −v _(i+1))*a*α  (11)

Which means the acceleration may be accomplished within the angleinterval a, a is gantry speed acceleration, and sign is the signfunction. The constraint of accelerated time may be defined as:v _(i) −v _(i+1)≤sign(v _(i) −v _(i+1))*a*ELST_(i)  (12)

Which means the acceleration may be accomplished within the energyswitch time ELST_(i), where ELST is the energy switch time within thei-th control point. Thus, the constraint of delta gantry speed betweencontrol point may be derived as:v _(i) −v _(i+1)≤max(sign(v _(i) −v _(i+1))*a(v _(i) −v_(i+1)),a*ELSt_(i))  (13)

The velocity/momentum change constraint between the control points orduring the treat delivery may be defined as:V _(i) −V _(i+1) =ΔV>user settings  (14)

To minimize the speed difference between the adjacent control point iand i+1, the SPArc-MPO iteratively (i) adds/deletes/re-distributes spotsand (ii) merges/redistributes/deletes/adds/modifies energy layers.

The electronic parameters, such as proton spot/energy/burst/lineirradiation or delivery sequence, may include (i) spot-switching time,(ii) spot drill time, (iii) energy layer switching time, and (iv)burst-switching time. The user defined parameters may include (i) adefined total SPArc delivery time/speed, e.g., 4 minutes, (ii) adelivery tolerance window, e.g., +/−1 degree (which may be equal to themachine parameter), and (iii) momentum/speed change during the treatmentdelivery.

At block 410, the calculated SPArc delivery time per control point 124is implemented using DSM_(arc). At block 412, the SPArc-MPO mayiteratively adjust the proton beam delivery time within and between thecontrol points 124 through (i) filtering, merging, re-distributingexisting energy layers, and/or adding new energy layers, (ii) filtering,merging, re-distributing existing proton spots, and/or adding new protonspots within and between the control points 124, and (iii) adjusting theMU or intensity of the proton spots within and between the controlpoints 124. Through the iterative SPArc optimization framework, theSPArc-MPO gradually adjusts the delivery time within and betweenadjacent control points 124 through direct mechanical parametersoptimization to ensure a smooth and efficient rotational proton arcdelivery which may fit a specific proton machine parameters such asmax/min gantry speed, acceleration/deceleration speed, and controllerlimitation between the control points 124.

At block 414, the method 400 returns to block 412 to continue theiterative optimization approach if the following conditions are notsatisfied: (i) total delivery meet the user setting, (ii) plan qualityis acceptable, (iii) momentum/velocity change meet the user setting, and(iv) connections between the control points meet the mechanicallimitations. If these conditions are satisfied, then at block 416, themethod 400 proceeds to the SPArc-MPO.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),FPGAs (field-programmable gate arrays), computer hardware, firmware,software, and/or combinations thereof. These various implementations caninclude implementation in one or more computer programs that areexecutable and/or interpretable on a programmable system including atleast one programmable processor, which may be special or generalpurpose, coupled to receive data and instructions from, and to transmitdata and instructions to, a storage system, at least one input device,and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for this programmableprocessor and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms “machine-readable medium” and“computer-readable medium” refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term “machine-readable signal” refers to any signal used to providemachine instructions and/or data to a programmable processor.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Moreover,subject matter described in this specification can be implemented as oneor more computer program products, i.e., one or more modules of computerprogram instructions encoded on a computer readable medium for executionby, or to control the operation of, data processing apparatus. Thecomputer readable medium can be a machine-readable storage device, amachine-readable storage substrate, a memory device, a composition ofmatter affecting a machine-readable propagated signal, or a combinationof one or more of them. The terms “data processing apparatus”,“computing device” and “computing processor” encompass all apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them. A propagated signal is an artificially generated signal, e.g.,a machine-generated electrical, optical, or electromagnetic signal thatis generated to encode information for transmission to suitable receiverapparatus.

A computer program (also known as an application, program, software,software application, script, or code) can be written in any form ofprogramming language, including compiled or interpreted languages, andit can be deployed in any form, including as a stand-alone program or asa module, component, subroutine, or other unit suitable for use in acomputing environment. A computer program does not necessarilycorrespond to a file in a file system. A program can be stored in aportion of a file that holds other programs or data (e.g., one or morescripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program can be deployed to be executed on onecomputer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit), or an ASIC specially designedto withstand the high radiation environment of space (known as“radiation hardened”, or “rad-hard”).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio player, a Global Positioning System (GPS)receiver, to name just a few. Computer readable media suitable forstoring computer program instructions and data include all forms ofnon-volatile memory, media and memory devices, including by way ofexample semiconductor memory devices, e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, e.g., internal hard disks or removabledisks; magneto optical disks; and CD ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,special purpose logic circuitry.

One or more aspects of the disclosure can be implemented in a computingsystem that includes a backend component, e.g., as a data server, orthat includes a middleware component, e.g., an application server, orthat includes a frontend component, e.g., a client computer having agraphical user interface or a Web browser through which a user caninteract with an implementation of the subject matter described in thisspecification, or any combination of one or more such backend,middleware, or frontend components. The components of the system can beinterconnected by any form or medium of digital data communication,e.g., a communication network. Examples of communication networksinclude a local area network (“LAN”) and a wide area network (“WAN”), aninter-network (e.g., the Internet), and peer-to-peer networks (e.g., adhoc peer-to-peer networks).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. In someimplementations, a server transmits data (e.g., an HTML page) to aclient device (e.g., for purposes of displaying data to and receivinguser input from a user interacting with the client device). Datagenerated at the client device (e.g., a result of the user interaction)can be received from the client device at the server.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the disclosure or of what maybe claimed, but rather as descriptions of features specific toparticular implementations of the disclosure. Certain features that aredescribed in this specification in the context of separateimplementations can also be implemented in combination in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation can also be implemented in multipleimplementations separately or in any suitable sub-combination. Moreover,although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multi-tasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

A number of implementations have been described. Nevertheless, it willbe understood that various modifications may be made without departingfrom the spirit and scope of the disclosure. Accordingly, otherimplementations are within the scope of the following claims. Forexample, the actions recited in the claims can be performed in adifferent order and still achieve desirable results.

What is claimed is:
 1. A method of optimizing delivery of a particlebeam at a target, wherein the particle beam is delivered from an outputdevice at a plurality of control points, the method comprising:delivering a substantially continuous particle beam about the pluralityof control points; iteratively adjusting a delivery time of thesubstantially continuous particle beam about the plurality of controlpoints; identifying an initial set of control points; providing a firstgroup of optimized beams, wherein each optimized beam of the first groupof optimized beams is configured to be output at a first control pointand steered towards one or more energy layers that are associated withone or more monitor unit (MU); providing a second group of optimizedbeams, each optimized beam of the second group of optimized beamsconfigured to be output at the first control point and having a reducednumber of energy layers; increasing a number of the initial controlpoints; and maintaining a total number of energy layers as one of equalto, more than, or less than a total number of energy layers associatedwith the initial control points during movement of the beam between theplurality of control points, wherein increasing the number of initialcontrol points comprises adding one or more adjacent control points toeach initial control point, the adjacent control point having a fractionnumber of the total energy layers or MU of the initial control point. 2.The method of claim 1, wherein the delivery time of the substantiallycontinuous particle beam is iteratively adjusted via one or more offiltering the one or more energy layers, merging the one or more energylayers, re-distributing the one or more energy layers, or adding newenergy layers to the one or more energy layers.
 3. The method of claim1, wherein the delivery time of the substantially continuous particlebeam is iteratively adjusted via one or more of filtering one or moreproton spots, merging the one or more proton spots, re-distributing theone or more proton spots, or adding new proton spots within and betweenthe one or more proton spots.
 4. The method of claim 1, wherein thedelivery time of the substantially continuous particle beam isiteratively adjusted via adjusting one or more of the MU of one or moreproton spots within and between the control points or an intensity ofthe one or more proton spots within and between the control points. 5.The method of claim 1, wherein the delivery time of the substantiallycontinuous particle beam is at least partially determined based on amachine-specific delivery sequence model (DSM_(arc)) that is calculatedusing a plurality of machine specific system parameters and a pluralityof user defined parameters.
 6. The method of claim 5, wherein theplurality of machine specific system parameters includes a deliverytolerance window, gantry mechanical limitations, and mechanism ofcontrol point connections.
 7. The method of claim 5, wherein theplurality of user defined parameters includes a defined total SPArcdelivery time or speed, a delivery tolerance window, and momentum orspeed change during delivery of the substantially continuous particlebeam.
 8. A method of optimizing delivery of a particle beam at a target,wherein the particle beam is delivered from an output device at aplurality of control points, the method comprising: delivering asubstantially continuous particle beam about the plurality of controlpoints; iteratively adjusting a delivery time of the substantiallycontinuous particle beam about the plurality of control points; andprocessing to undertake at least one of (i) pre-defining energy layersbased on one or both of the control points and a control point samplingfrequency, or (ii) sorting the energy layers.
 9. The method of claim 8,wherein the delivery time of the substantially continuous particle beamis iteratively adjusted via one or more of filtering the energy layers,merging the energy layers, re-distributing the energy layers, modifyingthe energy layers, and adding new energy layers to the energy layers.10. The method of claim 8, wherein the delivery time of thesubstantially continuous particle beam is iteratively adjusted via oneor more of filtering one or more proton spots, merging the one or moreproton spots, re-distributing the one or more proton spots, or addingnew proton spots within and between the one or more proton spots. 11.The method of claim 8, wherein the delivery time of the substantiallycontinuous particle beam is iteratively adjusted via adjusting one ormore of an MU of one or more proton spots within and between the controlpoints or an intensity of the one or more proton spots within andbetween the control points.
 12. The method of claim 8, wherein thedelivery time of the substantially continuous particle beam is at leastpartially determined based on a machine-specific delivery sequence model(DSM_(arc)) that is calculated using a plurality of machine specificsystem parameters and a plurality of user defined parameters.
 13. Themethod of claim 12, wherein the plurality of machine specific systemparameters includes a delivery tolerance window, gantry mechanicallimitations, and mechanism of control point connections.
 14. The methodof claim 12, wherein the plurality of user defined parameters includes adefined total SPArc delivery time or speed, a delivery tolerance window,and momentum or speed change during delivery of the substantiallycontinuous particle beam.
 15. A system comprising: data processinghardware; and memory hardware in communication with the data processinghardware, the memory hardware storing instructions that when executed onthe data processing hardware cause the data processing hardware toperform operations comprising: delivering a substantially continuousparticle beam at a target about a plurality of control points;iteratively adjusting a delivery time of the substantially continuousparticle beam about the plurality of control points; identifying aninitial set of control points; providing a first group of optimizedbeams, wherein each optimized beam of the first group of optimized beamsis configured to be output at a first control point and steered towardsone or more energy layers that are associated with one or more monitorunit (MU); providing a second group of optimized beams, each optimizedbeam of the second group of optimized beams configured to be output atthe first control point and having a reduced number of energy layers;increasing a number of the initial control points; and maintaining atotal number of energy layers as one of equal to, more than, or lessthan a total number of energy layers associated with the initial controlpoints during movement of the beam between the plurality of controlpoints, wherein increasing the number of initial control pointscomprises adding one or more adjacent control points to each initialcontrol point, the adjacent control point having a fraction number ofthe total energy layers or MU of the initial control point.
 16. Thesystem of claim 15, wherein the delivery time of the substantiallycontinuous particle beam is iteratively adjusted via one or more offiltering the one or more energy layers, merging the one or more energylayers, re-distributing the one or more energy layers, or adding newenergy layers to the one or more energy layers.
 17. The system of claim15, wherein the delivery time of the substantially continuous particlebeam is iteratively adjusted via one or more of filtering one or moreproton spots, merging the one or more proton spots, re-distributing theone or more proton spots, or adding new proton spots within and betweenthe one or more proton spots.
 18. The system of claim 15, wherein thedelivery time of the substantially continuous particle beam isiteratively adjusted via adjusting one or more of the MU of one or moreproton spots within and between the control points or an intensity ofthe one or more proton spots within and between the control points. 19.The system of claim 15, wherein the delivery time of the substantiallycontinuous particle beam is at least partially determined based on amachine-specific delivery sequence model (DSM_(arc)) that is calculatedusing a plurality of machine specific system parameters and a pluralityof user defined parameters.
 20. The system of claim 19, wherein theplurality of machine specific system parameters includes a deliverytolerance window, gantry mechanical limitations, and mechanism ofcontrol point connections.